In this Wiki
Data integration is one of the oldest disciplines in computer science, with a long history of technologies, tools and approaches. Since the early days of CORBA, RPC and monolithic Enterprise Data Integration platforms, we’ve progressed to leaner, flexible architectures such as SOA, ESB and JDBC, through to today’s cloud-based iPaaS platforms which promise to connect and migrate massive quantities of data in minutes.
We all need to learn from this rich history of knowledge, and keep up with new technologies which are in constant flux. At Alooma, we built a world-class platform that makes data integration easier, and in a similar vein, we wanted to make the massive knowledge around data integration more easily accessible.
So we created this website - a wiki that pulls together all the concepts, technologies and best practices on data integration from around the world. It’s a carefully curated directory of thousands of resources written by individuals around the world, which we plan to make the world’s biggest source of knowledge on data integration.
Resources on primary uses and pain data integration solves in modern enterprises, including big data integration, EAI, MDI, hybrid cloud data migration, and virtualization.
Resources on all aspects of aarchitecting and managing organizational data, including data modelling, schema mapping, data transformation, SOA, ESB, edh, messaging, CEP, etl , and data ingestion.
Resources on technologies that help store, manage and integrate different data sources, including iPaaS like Ensighten, Alooma and Zapier, on-premise integration tools like Talend, Pentaho and Mule, and stream processing tools such as Kafka.
Resources on Extract Transform Load (ETL) practices, including ETL architecture and key concepts, best practices, ETL testing, performance, training and certifications.
Resources about data warehouse technology, used to store large amounts of data to facilitate processing, analysis and visualization. Including:
- Data warehousing best practices, concepts and architecture
- Data warehousing training and certifications
- Enterprise data architecture - OLAP and OLTP
- Data warehouse tools and services - Amazon Redshift, Google BigQuery, Snowflake, IBM PureData, SAP Business Warehouse, Oracle Exadata, and many more
- Using databases for data warehousing - storing large volumes of data using MemSQL, Oracl, MongoDB, and MySQL
Resources about key roles in managing and making use of organizational data, including skills and job descriptions, job listings, salaries, training and certifications:
- Data Engineer
- Data Scientist and Big Data Analyst
- BI Developer
- Chief Data Officer (CDO)
Easily Move Data Into Your Data Warehouse
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